import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				ProphetNetTokenizer
   __lowerCAmelCase           =				False







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    super().setUp()

    UpperCamelCase			  =      [
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """[PAD]""",
        """[MASK]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    UpperCamelCase			  =      os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["""vocab_file"""]				)
    with open(self.vocab_file   ,   """w"""   ,   encoding="""utf-8"""				) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      """UNwant\u00E9d,running"""
    UpperCamelCase			  =      """unwanted, running"""
    return input_text, output_text







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.tokenizer_class(self.vocab_file				)

    UpperCamelCase			  =      tokenizer.tokenize("""UNwant\u00E9d,running"""				)
    self.assertListEqual(lowerCamelCase_   ,   ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]				)
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_				)   ,   [9, 6, 7, 12, 10, 11]				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      BasicTokenizer()

    self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""				)   ,   ["""ah""", """\u535A""", """\u63A8""", """zz"""]				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      BasicTokenizer(do_lower_case=lowerCamelCase_				)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """				)   ,   ["""hello""", """!""", """how""", """are""", """you""", """?"""]				)
    self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""				)   ,   ["""hello"""]				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      BasicTokenizer(do_lower_case=lowerCamelCase_   ,   strip_accents=lowerCamelCase_				)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """				)   ,   ["""hällo""", """!""", """how""", """are""", """you""", """?"""]				)
    self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""				)   ,   ["""h\u00E9llo"""]				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      BasicTokenizer(do_lower_case=lowerCamelCase_   ,   strip_accents=lowerCamelCase_				)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """				)   ,   ["""hallo""", """!""", """how""", """are""", """you""", """?"""]				)
    self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""				)   ,   ["""hello"""]				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      BasicTokenizer(do_lower_case=lowerCamelCase_				)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """				)   ,   ["""hallo""", """!""", """how""", """are""", """you""", """?"""]				)
    self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""				)   ,   ["""hello"""]				)







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      BasicTokenizer(do_lower_case=lowerCamelCase_				)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """				)   ,   ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      BasicTokenizer(do_lower_case=lowerCamelCase_   ,   strip_accents=lowerCamelCase_				)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """				)   ,   ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      BasicTokenizer(do_lower_case=lowerCamelCase_   ,   strip_accents=lowerCamelCase_				)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """				)   ,   ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      BasicTokenizer(do_lower_case=lowerCamelCase_   ,   never_split=["""[UNK]"""]				)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""				)   ,   ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

    UpperCamelCase			  =      {}
    for i, token in enumerate(lowerCamelCase_				):
     UpperCamelCase			  =      i
    UpperCamelCase			  =      WordpieceTokenizer(vocab=lowerCamelCase_   ,   unk_token="""[UNK]"""				)

    self.assertListEqual(tokenizer.tokenize(""""""				)   ,   []				)

    self.assertListEqual(tokenizer.tokenize("""unwanted running"""				)   ,   ["""un""", """##want""", """##ed""", """runn""", """##ing"""]				)

    self.assertListEqual(tokenizer.tokenize("""unwantedX running"""				)   ,   ["""[UNK]""", """runn""", """##ing"""]				)







   @require_torch
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""				)

    UpperCamelCase			  =      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    UpperCamelCase			  =      [1037, 2146, 2_0423, 2005, 7680, 7849, 3989, 1012, 102]
    UpperCamelCase			  =      tokenizer(lowerCamelCase_   ,   padding=lowerCamelCase_   ,   return_tensors="""pt"""				)
    self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)
    UpperCamelCase			  =      list(batch.input_ids.numpy()[0]				)
    self.assertListEqual(lowerCamelCase_   ,   lowerCamelCase_				)

    self.assertEqual((2, 9)   ,   batch.input_ids.shape				)
    self.assertEqual((2, 9)   ,   batch.attention_mask.shape				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    self.assertTrue(_is_whitespace(""" """				)				)
    self.assertTrue(_is_whitespace("""\t"""				)				)
    self.assertTrue(_is_whitespace("""\r"""				)				)
    self.assertTrue(_is_whitespace("""\n"""				)				)
    self.assertTrue(_is_whitespace("""\u00A0"""				)				)

    self.assertFalse(_is_whitespace("""A"""				)				)
    self.assertFalse(_is_whitespace("""-"""				)				)







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    self.assertTrue(_is_control("""\u0005"""				)				)

    self.assertFalse(_is_control("""A"""				)				)
    self.assertFalse(_is_control(""" """				)				)
    self.assertFalse(_is_control("""\t"""				)				)
    self.assertFalse(_is_control("""\r"""				)				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    self.assertTrue(_is_punctuation("""-"""				)				)
    self.assertTrue(_is_punctuation("""$"""				)				)
    self.assertTrue(_is_punctuation("""`"""				)				)
    self.assertTrue(_is_punctuation("""."""				)				)

    self.assertFalse(_is_punctuation("""A"""				)				)
    self.assertFalse(_is_punctuation(""" """				)				)







   @slow
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""				)

    UpperCamelCase			  =      tokenizer.encode("""sequence builders"""   ,   add_special_tokens=lowerCamelCase_				)
    UpperCamelCase			  =      tokenizer.encode("""multi-sequence build"""   ,   add_special_tokens=lowerCamelCase_				)

    UpperCamelCase			  =      tokenizer.build_inputs_with_special_tokens(lowerCamelCase_				)
    UpperCamelCase			  =      tokenizer.build_inputs_with_special_tokens(lowerCamelCase_   ,   lowerCamelCase_				)

    assert encoded_sentence == text + [102]
    assert encoded_pair == text + [102] + text_a + [102]

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				["""image_processor""", """tokenizer"""]
   __lowerCAmelCase           =				"""LayoutLMv3ImageProcessor"""
   __lowerCAmelCase           =				("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   lowerCamelCase_						:						Dict=None   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""   ,   lowerCamelCase_   ,   )
     UpperCamelCase			  =      kwargs.pop("""feature_extractor"""				)

    UpperCamelCase			  =      image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`."""				)
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`."""				)

    super().__init__(lowerCamelCase_   ,   lowerCamelCase_				)







   def __call__(     self						:						int   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,   lowerCamelCase_						:						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,   lowerCamelCase_						:						Union[List[List[int]], List[List[List[int]]]] = None   ,   lowerCamelCase_						:						Optional[Union[List[int], List[List[int]]]] = None   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Union[bool, str, PaddingStrategy] = False   ,   lowerCamelCase_						:						Union[bool, str, TruncationStrategy] = None   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						int = 0   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Optional[Union[str, TensorType]] = None   ,   **lowerCamelCase_						:						Optional[int]   ,   ):





    """simple docstring"""


    if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""				)

    if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""				)

    # first, apply the image processor
    UpperCamelCase			  =      self.image_processor(images=lowerCamelCase_   ,   return_tensors=lowerCamelCase_				)

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      UpperCamelCase			  =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
     UpperCamelCase			  =      features["""words"""]

    UpperCamelCase			  =      self.tokenizer(
        text=text if text is not None else features["""words"""]   ,   text_pair=text_pair if text_pair is not None else None   ,   boxes=boxes if boxes is not None else features["""boxes"""]   ,   word_labels=lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_   ,   padding=lowerCamelCase_   ,   truncation=lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   stride=lowerCamelCase_   ,   pad_to_multiple_of=lowerCamelCase_   ,   return_token_type_ids=lowerCamelCase_   ,   return_attention_mask=lowerCamelCase_   ,   return_overflowing_tokens=lowerCamelCase_   ,   return_special_tokens_mask=lowerCamelCase_   ,   return_offsets_mapping=lowerCamelCase_   ,   return_length=lowerCamelCase_   ,   verbose=lowerCamelCase_   ,   return_tensors=lowerCamelCase_   ,   **lowerCamelCase_   ,   )

    # add pixel values
    UpperCamelCase			  =      features.pop("""pixel_values"""				)
    if return_overflowing_tokens is True:
     UpperCamelCase			  =      self.get_overflowing_images(lowerCamelCase_   ,   encoded_inputs["""overflow_to_sample_mapping"""]				)
    UpperCamelCase			  =      images

    return encoded_inputs







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      []
    for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]				)

    if len(lowerCamelCase_				) != len(lowerCamelCase_				):
     raise ValueError(
         """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
         f""" {len(lowerCamelCase_				)} and {len(lowerCamelCase_				)}"""				)

    return images_with_overflow







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   *lowerCamelCase_						:						Any   ,   **lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    return self.tokenizer.batch_decode(*lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   *lowerCamelCase_						:						Dict   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    return self.tokenizer.decode(*lowerCamelCase_   ,   **lowerCamelCase_				)







   @property
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    return ["input_ids", "bbox", "attention_mask", "pixel_values"]







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,   lowerCamelCase_   ,   )
    return self.image_processor_class







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,   lowerCamelCase_   ,   )
    return self.image_processor
